Variational Bayesian approach for ARX systems with missing observations and varying time-delays

This paper develops a variational Bayesian approach for identifying ARX models with missing observations and varying time-delays. The outputs of the ARX models are subject to both slow sampling rates and communication delays. The unknown missing observations which are used in the variational Bayesia...
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Autor*in:	Chen, Jing [verfasserIn] Huang, Biao [verfasserIn] Ding, Feng [verfasserIn] Gu, Ya [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Parameter estimation Variational Bayesian approach Varying time-delay Missing observations Kalman filtering method

Übergeordnetes Werk:	Enthalten in: Automatica - Amsterdam [u.a.] : Elsevier, Pergamon Press, 1963, 94, Seite 194-204
Übergeordnetes Werk:	volume:94 ; pages:194-204

DOI / URN:	10.1016/j.automatica.2018.04.003

Katalog-ID:	ELV001790579

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245	1	0	\|a Variational Bayesian approach for ARX systems with missing observations and varying time-delays
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337			\|a Computermedien \|b c \|2 rdamedia
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520			\|a This paper develops a variational Bayesian approach for identifying ARX models with missing observations and varying time-delays. The outputs of the ARX models are subject to both slow sampling rates and communication delays. The unknown missing observations which are used in the variational Bayesian approach can be estimated by a modified Kalman filter, and based on the estimated missing observations and available data, the unknown parameters and the varying time-delays can be estimated by using the variational Bayesian approach. The simulation results demonstrate that the variational Bayesian method is effective.
650		4	\|a Parameter estimation
650		4	\|a Variational Bayesian approach
650		4	\|a Varying time-delay
650		4	\|a Missing observations
650		4	\|a Kalman filtering method
700	1		\|a Huang, Biao \|e verfasserin \|4 aut
700	1		\|a Ding, Feng \|e verfasserin \|4 aut
700	1		\|a Gu, Ya \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Automatica \|d Amsterdam [u.a.] : Elsevier, Pergamon Press, 1963 \|g 94, Seite 194-204 \|h Online-Ressource \|w (DE-627)266886388 \|w (DE-600)1468509-7 \|w (DE-576)094478724 \|x 0005-1098 \|7 nnns
773	1	8	\|g volume:94 \|g pages:194-204
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912			\|a GBV_ILN_100
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
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912			\|a GBV_ILN_151
912			\|a GBV_ILN_224
912			\|a GBV_ILN_266
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
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912			\|a GBV_ILN_2021
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912			\|a GBV_ILN_2044
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912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
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912			\|a GBV_ILN_2122
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912			\|a GBV_ILN_2153
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912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
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912			\|a GBV_ILN_2522
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
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912			\|a GBV_ILN_4393
936	b	k	\|a 31.00 \|j Mathematik: Allgemeines
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matchkey_str	article:00051098:2018----::aitoabysaapocfrrssesihisnosrai
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allfields	10.1016/j.automatica.2018.04.003 doi (DE-627)ELV001790579 (ELSEVIER)S0005-1098(18)30188-2 DE-627 ger DE-627 rda eng 000 620 DE-600 31.00 bkl 50.20 bkl Chen, Jing verfasserin aut Variational Bayesian approach for ARX systems with missing observations and varying time-delays 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper develops a variational Bayesian approach for identifying ARX models with missing observations and varying time-delays. The outputs of the ARX models are subject to both slow sampling rates and communication delays. The unknown missing observations which are used in the variational Bayesian approach can be estimated by a modified Kalman filter, and based on the estimated missing observations and available data, the unknown parameters and the varying time-delays can be estimated by using the variational Bayesian approach. The simulation results demonstrate that the variational Bayesian method is effective. Parameter estimation Variational Bayesian approach Varying time-delay Missing observations Kalman filtering method Huang, Biao verfasserin aut Ding, Feng verfasserin aut Gu, Ya verfasserin aut Enthalten in Automatica Amsterdam [u.a.] : Elsevier, Pergamon Press, 1963 94, Seite 194-204 Online-Ressource (DE-627)266886388 (DE-600)1468509-7 (DE-576)094478724 0005-1098 nnns volume:94 pages:194-204 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_266 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 31.00 Mathematik: Allgemeines 50.20 Automatisierungstechnik AR 94 194-204
spelling	10.1016/j.automatica.2018.04.003 doi (DE-627)ELV001790579 (ELSEVIER)S0005-1098(18)30188-2 DE-627 ger DE-627 rda eng 000 620 DE-600 31.00 bkl 50.20 bkl Chen, Jing verfasserin aut Variational Bayesian approach for ARX systems with missing observations and varying time-delays 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper develops a variational Bayesian approach for identifying ARX models with missing observations and varying time-delays. The outputs of the ARX models are subject to both slow sampling rates and communication delays. The unknown missing observations which are used in the variational Bayesian approach can be estimated by a modified Kalman filter, and based on the estimated missing observations and available data, the unknown parameters and the varying time-delays can be estimated by using the variational Bayesian approach. The simulation results demonstrate that the variational Bayesian method is effective. Parameter estimation Variational Bayesian approach Varying time-delay Missing observations Kalman filtering method Huang, Biao verfasserin aut Ding, Feng verfasserin aut Gu, Ya verfasserin aut Enthalten in Automatica Amsterdam [u.a.] : Elsevier, Pergamon Press, 1963 94, Seite 194-204 Online-Ressource (DE-627)266886388 (DE-600)1468509-7 (DE-576)094478724 0005-1098 nnns volume:94 pages:194-204 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_266 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 31.00 Mathematik: Allgemeines 50.20 Automatisierungstechnik AR 94 194-204
allfields_unstemmed	10.1016/j.automatica.2018.04.003 doi (DE-627)ELV001790579 (ELSEVIER)S0005-1098(18)30188-2 DE-627 ger DE-627 rda eng 000 620 DE-600 31.00 bkl 50.20 bkl Chen, Jing verfasserin aut Variational Bayesian approach for ARX systems with missing observations and varying time-delays 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper develops a variational Bayesian approach for identifying ARX models with missing observations and varying time-delays. The outputs of the ARX models are subject to both slow sampling rates and communication delays. The unknown missing observations which are used in the variational Bayesian approach can be estimated by a modified Kalman filter, and based on the estimated missing observations and available data, the unknown parameters and the varying time-delays can be estimated by using the variational Bayesian approach. The simulation results demonstrate that the variational Bayesian method is effective. Parameter estimation Variational Bayesian approach Varying time-delay Missing observations Kalman filtering method Huang, Biao verfasserin aut Ding, Feng verfasserin aut Gu, Ya verfasserin aut Enthalten in Automatica Amsterdam [u.a.] : Elsevier, Pergamon Press, 1963 94, Seite 194-204 Online-Ressource (DE-627)266886388 (DE-600)1468509-7 (DE-576)094478724 0005-1098 nnns volume:94 pages:194-204 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_266 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 31.00 Mathematik: Allgemeines 50.20 Automatisierungstechnik AR 94 194-204
allfieldsGer	10.1016/j.automatica.2018.04.003 doi (DE-627)ELV001790579 (ELSEVIER)S0005-1098(18)30188-2 DE-627 ger DE-627 rda eng 000 620 DE-600 31.00 bkl 50.20 bkl Chen, Jing verfasserin aut Variational Bayesian approach for ARX systems with missing observations and varying time-delays 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper develops a variational Bayesian approach for identifying ARX models with missing observations and varying time-delays. The outputs of the ARX models are subject to both slow sampling rates and communication delays. The unknown missing observations which are used in the variational Bayesian approach can be estimated by a modified Kalman filter, and based on the estimated missing observations and available data, the unknown parameters and the varying time-delays can be estimated by using the variational Bayesian approach. The simulation results demonstrate that the variational Bayesian method is effective. Parameter estimation Variational Bayesian approach Varying time-delay Missing observations Kalman filtering method Huang, Biao verfasserin aut Ding, Feng verfasserin aut Gu, Ya verfasserin aut Enthalten in Automatica Amsterdam [u.a.] : Elsevier, Pergamon Press, 1963 94, Seite 194-204 Online-Ressource (DE-627)266886388 (DE-600)1468509-7 (DE-576)094478724 0005-1098 nnns volume:94 pages:194-204 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_266 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 31.00 Mathematik: Allgemeines 50.20 Automatisierungstechnik AR 94 194-204
allfieldsSound	10.1016/j.automatica.2018.04.003 doi (DE-627)ELV001790579 (ELSEVIER)S0005-1098(18)30188-2 DE-627 ger DE-627 rda eng 000 620 DE-600 31.00 bkl 50.20 bkl Chen, Jing verfasserin aut Variational Bayesian approach for ARX systems with missing observations and varying time-delays 2018 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper develops a variational Bayesian approach for identifying ARX models with missing observations and varying time-delays. The outputs of the ARX models are subject to both slow sampling rates and communication delays. The unknown missing observations which are used in the variational Bayesian approach can be estimated by a modified Kalman filter, and based on the estimated missing observations and available data, the unknown parameters and the varying time-delays can be estimated by using the variational Bayesian approach. The simulation results demonstrate that the variational Bayesian method is effective. Parameter estimation Variational Bayesian approach Varying time-delay Missing observations Kalman filtering method Huang, Biao verfasserin aut Ding, Feng verfasserin aut Gu, Ya verfasserin aut Enthalten in Automatica Amsterdam [u.a.] : Elsevier, Pergamon Press, 1963 94, Seite 194-204 Online-Ressource (DE-627)266886388 (DE-600)1468509-7 (DE-576)094478724 0005-1098 nnns volume:94 pages:194-204 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_266 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 31.00 Mathematik: Allgemeines 50.20 Automatisierungstechnik AR 94 194-204
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author	Chen, Jing
spellingShingle	Chen, Jing ddc 000 bkl 31.00 bkl 50.20 misc Parameter estimation misc Variational Bayesian approach misc Varying time-delay misc Missing observations misc Kalman filtering method Variational Bayesian approach for ARX systems with missing observations and varying time-delays
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topic_title	000 620 DE-600 31.00 bkl 50.20 bkl Variational Bayesian approach for ARX systems with missing observations and varying time-delays Parameter estimation Variational Bayesian approach Varying time-delay Missing observations Kalman filtering method
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title	Variational Bayesian approach for ARX systems with missing observations and varying time-delays
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title_full	Variational Bayesian approach for ARX systems with missing observations and varying time-delays
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author_browse	Chen, Jing Huang, Biao Ding, Feng Gu, Ya
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title_sort	variational bayesian approach for arx systems with missing observations and varying time-delays
title_auth	Variational Bayesian approach for ARX systems with missing observations and varying time-delays
abstract	This paper develops a variational Bayesian approach for identifying ARX models with missing observations and varying time-delays. The outputs of the ARX models are subject to both slow sampling rates and communication delays. The unknown missing observations which are used in the variational Bayesian approach can be estimated by a modified Kalman filter, and based on the estimated missing observations and available data, the unknown parameters and the varying time-delays can be estimated by using the variational Bayesian approach. The simulation results demonstrate that the variational Bayesian method is effective.
abstractGer	This paper develops a variational Bayesian approach for identifying ARX models with missing observations and varying time-delays. The outputs of the ARX models are subject to both slow sampling rates and communication delays. The unknown missing observations which are used in the variational Bayesian approach can be estimated by a modified Kalman filter, and based on the estimated missing observations and available data, the unknown parameters and the varying time-delays can be estimated by using the variational Bayesian approach. The simulation results demonstrate that the variational Bayesian method is effective.
abstract_unstemmed	This paper develops a variational Bayesian approach for identifying ARX models with missing observations and varying time-delays. The outputs of the ARX models are subject to both slow sampling rates and communication delays. The unknown missing observations which are used in the variational Bayesian approach can be estimated by a modified Kalman filter, and based on the estimated missing observations and available data, the unknown parameters and the varying time-delays can be estimated by using the variational Bayesian approach. The simulation results demonstrate that the variational Bayesian method is effective.
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title_short	Variational Bayesian approach for ARX systems with missing observations and varying time-delays
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author2	Huang, Biao Ding, Feng Gu, Ya
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doi_str	10.1016/j.automatica.2018.04.003
up_date	2024-07-06T22:35:02.830Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV001790579</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230524131321.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230428s2018 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.automatica.2018.04.003</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV001790579</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0005-1098(18)30188-2</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">000</subfield><subfield code="a">620</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">31.00</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">50.20</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Chen, Jing</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Variational Bayesian approach for ARX systems with missing observations and varying time-delays</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This paper develops a variational Bayesian approach for identifying ARX models with missing observations and varying time-delays. The outputs of the ARX models are subject to both slow sampling rates and communication delays. The unknown missing observations which are used in the variational Bayesian approach can be estimated by a modified Kalman filter, and based on the estimated missing observations and available data, the unknown parameters and the varying time-delays can be estimated by using the variational Bayesian approach. The simulation results demonstrate that the variational Bayesian method is effective.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Parameter estimation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Variational Bayesian approach</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Varying time-delay</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Missing observations</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Kalman filtering method</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Huang, Biao</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ding, Feng</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" 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Variational Bayesian approach for ARX systems with missing observations and varying time-delays

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